In the present field of art, stock market trading systems are based upon a variety of automated methods, formulas and algorithms with which to predict the dynamics and trends in market behavior. However, prior art stock trading systems fail to recognize the value of automating stock trading decisions based upon statistical correlations which are historically proven to exist between certain company events, actions and metrics (which are extracted through natural language processing via associated templates and changes and dynamics in stock prices). The present invention uses such correlations to develop a stock market prediction model.